455a48409d
Add Memori (https://github.com/MemoriLabs/Memori) integration for persistent cross-session memory capabilities in both create_agent and create_deep_agent. ## New Files - agent/memori_config.py: MemoriConfig dataclass for configuration - agent/memori_manager.py: MemoriManager for connection and instance management - agent/memori_middleware.py: MemoriMiddleware for memory recall/storage - tests/: Unit tests for Memori components ## Modified Files - agent/agent_config.py: Added enable_memori, memori_semantic_search_top_k, etc. - agent/deep_assistant.py: Integrated MemoriMiddleware into init_agent() - utils/settings.py: Added MEMORI_* environment variables - pyproject.toml: Added memori>=3.1.0 dependency ## Features - Semantic memory search with configurable top-k and threshold - Multi-tenant isolation (entity_id=user, process_id=bot, session_id) - Memory injection into system prompt - Background asynchronous memory augmentation - Graceful degradation when Memori is unavailable 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
import os
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# 必填参数
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# API Settings
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BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
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MASTERKEY = os.getenv("MASTERKEY", "master")
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FASTAPI_URL = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
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# LLM Token Settings
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MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 262144))
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MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
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# 可选参数
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# Summarization Settings
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SUMMARIZATION_MAX_TOKENS = MAX_CONTEXT_TOKENS - MAX_OUTPUT_TOKENS - 1000
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SUMMARIZATION_MESSAGES_TO_KEEP = int(os.getenv("SUMMARIZATION_MESSAGES_TO_KEEP", 20))
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# Agent Cache Settings
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TOOL_CACHE_MAX_SIZE = int(os.getenv("TOOL_CACHE_MAX_SIZE", 20))
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TOOL_CACHE_TTL = int(os.getenv("TOOL_CACHE_TTL", 180))
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TOOL_CACHE_AUTO_RENEW = os.getenv("TOOL_CACHE_AUTO_RENEW", "true") == "true"
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# Project Settings
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PROJECT_DATA_DIR = os.getenv("PROJECT_DATA_DIR", "./projects/data")
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SKILLS_DIR = os.getenv("SKILLS_DIR", "./skills")
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# Tokenizer Settings
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TOKENIZERS_PARALLELISM = os.getenv("TOKENIZERS_PARALLELISM", "true")
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# Embedding Model Settings
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SENTENCE_TRANSFORMER_MODEL = os.getenv("SENTENCE_TRANSFORMER_MODEL", "TaylorAI/gte-tiny")
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# Tool Output Length Control Settings
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TOOL_OUTPUT_MAX_LENGTH = int(SUMMARIZATION_MAX_TOKENS/4)
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TOOL_OUTPUT_TRUNCATION_STRATEGY = os.getenv("TOOL_OUTPUT_TRUNCATION_STRATEGY", "smart")
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# THINKING ENABLE
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DEFAULT_THINKING_ENABLE = os.getenv("DEFAULT_THINKING_ENABLE", "true") == "true"
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# MCP Tool Timeout Settings
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MCP_HTTP_TIMEOUT = int(os.getenv("MCP_HTTP_TIMEOUT", 60)) # HTTP 请求超时（秒）
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MCP_SSE_READ_TIMEOUT = int(os.getenv("MCP_SSE_READ_TIMEOUT", 300)) # SSE 读取超时（秒）
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# ============================================================
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# PostgreSQL Checkpoint Configuration
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# ============================================================
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# PostgreSQL 连接字符串
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# 格式: postgresql://user:password@host:port/database
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#CHECKPOINT_DB_URL = os.getenv("CHECKPOINT_DB_URL", "postgresql://postgres:AeEGDB0b7Z5GK0E2tblt@dev-circleo-pg.celp3nik7oaq.ap-northeast-1.rds.amazonaws.com:5432/gptbase")
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CHECKPOINT_DB_URL = os.getenv("CHECKPOINT_DB_URL", "postgresql://moshui:@localhost:5432/moshui")
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# 连接池大小
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# 同时可以持有的最大连接数
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CHECKPOINT_POOL_SIZE = int(os.getenv("CHECKPOINT_POOL_SIZE", "20"))
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# Checkpoint 自动清理配置
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# 是否启用自动清理旧 session
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CHECKPOINT_CLEANUP_ENABLED = os.getenv("CHECKPOINT_CLEANUP_ENABLED", "true") == "true"
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# 清理多少天前未活动的 thread（天数）
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CHECKPOINT_CLEANUP_INACTIVE_DAYS = int(os.getenv("CHECKPOINT_CLEANUP_INACTIVE_DAYS", "3"))
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# 清理间隔（小时）
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# 每隔多少小时执行一次清理任务
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CHECKPOINT_CLEANUP_INTERVAL_HOURS = int(os.getenv("CHECKPOINT_CLEANUP_INTERVAL_HOURS", "24"))
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# ============================================================
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# Memori 长期记忆配置
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# ============================================================
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# Memori 功能开关（全局）
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MEMORI_ENABLED = os.getenv("MEMORI_ENABLED", "false") == "true"
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# Memori API 密钥（用于高级增强功能）
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MEMORI_API_KEY = os.getenv("MEMORI_API_KEY", "")
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# 语义搜索配置
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# 召回记忆数量
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MEMORI_SEMANTIC_SEARCH_TOP_K = int(os.getenv("MEMORI_SEMANTIC_SEARCH_TOP_K", "5"))
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# 相关性阈值（0.0 - 1.0）
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MEMORI_SEMANTIC_SEARCH_THRESHOLD = float(os.getenv("MEMORI_SEMANTIC_SEARCH_THRESHOLD", "0.7"))
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# 搜索嵌入限制
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MEMORI_SEMANTIC_SEARCH_EMBEDDINGS_LIMIT = int(os.getenv("MEMORI_SEMANTIC_SEARCH_EMBEDDINGS_LIMIT", "1000"))
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# 记忆注入配置
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# 是否将记忆注入到系统提示
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MEMORI_INJECT_TO_SYSTEM_PROMPT = os.getenv("MEMORI_INJECT_TO_SYSTEM_PROMPT", "true") == "true"
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# 增强配置
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# 是否启用后台增强
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MEMORI_AUGMENTATION_ENABLED = os.getenv("MEMORI_AUGMENTATION_ENABLED", "true") == "true"
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# 增强等待超时（秒），None 表示后台异步执行
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MEMORI_AUGMENTATION_WAIT_TIMEOUT = os.getenv("MEMORI_AUGMENTATION_WAIT_TIMEOUT")
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if MEMORI_AUGMENTATION_WAIT_TIMEOUT:
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MEMORI_AUGMENTATION_WAIT_TIMEOUT = float(MEMORI_AUGMENTATION_WAIT_TIMEOUT)
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else:
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MEMORI_AUGMENTATION_WAIT_TIMEOUT = None
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# 嵌入模型（多语言支持）
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MEMORI_EMBEDDING_MODEL = os.getenv(
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"MEMORI_EMBEDDING_MODEL",
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"paraphrase-multilingual-MiniLM-L12-v2"
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)
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